An abnormally enlarged aorta—also called aortic aneurysm—can tear or rupture and cause sudden cardiac death without any warning.
Until now.
Researchers Massachusetts General Hospital (MGH) have used deep learning to analyze how genetics could impact aorta size, and their findings may point to new preventive and therapeutic targets.
For the study, the team applied deep learning techniques to data collected from a UK Biobank study that performed multiple magnetic resonance imaging tests of the heart and aorta in more than 40,000 individuals.
The findings of their research were published in Nature Genetics.
“There were no aortic measurements provided by the UK Biobank, and we wanted to read the aortic diameter in all of the images collected,” explained lead author James Pirruccello, MD, a cardiologist at MGH and an instructor in medicine at Harvard Medical School, in a statement accompanying the report. “That is very hard for a human to do because it would take a long time, which motivated our use of deep learning models to do this process at a large scale.”
The researchers trained deep learning models to evaluate the dimensions of the ascending and descending sections of the aorta in 4.6 million cardiac images. The team then analyzed the study participants’ genes to identify variations in 82 genetic regions (or loci) linked to the diameter of the ascending aorta and 47 linked to the diameter of the descending aorta. Some of the loci were near genes with known associations with aortic disease.
“When we added up the genetic variants into what’s called a polygenic score, people with a higher score were more likely to be diagnosed with aortic aneurysm by a doctor,” noted Pirruccello. “This suggests that, after further development and testing, such a score might one day be useful to help us identify people at high risk of an aneurysm. The genetic loci that we discovered also offer a useful starting point for trying to identify new drug targets for aortic enlargement.”
In addition, Pirruccello suggested that the findings also provide supportive evidence that deep learning and other machine learning methods can help accelerate scientific analyses of complex biomedical data such as imaging results.
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